BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 97 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/Kalman/kalman_forward_backward.m b/sourcecodes/bnt-master/Kalman/kalman_forward_backward.m
new file mode 100644
index 00000000..9df240c4
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+++ b/sourcecodes/bnt-master/Kalman/kalman_forward_backward.m
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+% KALMAN_FORWARD_BACKWARD		Forward Backward Propogation in Information Form 
+%
+%
+% Note	:
+%
+%  M file accompanying my technical note
+%
+%    A Technique for Painless Derivation of Kalman Filtering Recursions
+%
+% available from http://www.mbfys.kun.nl/~cemgil/papers/painless-kalman.ps
+% 
+
+% Uses :
+
+% Change History :
+% Date		Time		Prog	Note
+% 07-Jun-2001	 2:24 PM	ATC	Created under MATLAB 5.3.1.29215a (R11.1)
+
+% ATC = Ali Taylan Cemgil,
+% SNN - University of Nijmegen, Department of Medical Physics and Biophysics
+% e-mail : cemgil@mbfys.kun.nl 
+
+A = [1 1;0 1];
+C = [1 0];
+Q = eye(2)*0.01^2;
+R = 0.001^2;
+mu1 = [0;1];
+P1 = 3*Q;
+
+inv_Q = inv(Q);
+inv_R = inv(R);
+
+y = [0 1.1 2 2.95 3.78];
+
+T = length(y);
+L = size(Q,1);
+
+%%%%% Forward message Passing 
+h_f = zeros(L, T);
+K_f = zeros(L, L, T);
+g_f = zeros(1, T);
+h_f_pre = zeros(L, T);
+K_f_pre = zeros(L, L, T);
+g_f_pre = zeros(1, T);
+
+
+K_f_pre(:, :, 1) = inv(P1);
+h_f_pre(:,1) = K_f_pre(:, :, 1)*mu1;
+g_f_pre(1) = -0.5*log(det(2*pi*P1)) - 0.5*mu1'*inv(P1)*mu1;
+
+for i=1:T,
+  h_f(:,i) = h_f_pre(:,i) + C'*inv_R*y(:,i);
+  K_f(:,:,i) = K_f_pre(:,:,i) + C'*inv_R*C;
+  g_f(i) = g_f_pre(i) -0.5*log(det(2*pi*R)) - 0.5*y(:,i)'*inv_R*y(:,i);
+  if i<T,
+    M = inv(A'*inv_Q*A + K_f(:,:,i));
+    h_f_pre(:,i+1) = inv_Q*A*M*h_f(:,i);
+    K_f_pre(:,:,i+1) = inv_Q - inv_Q*A*M*A'*inv_Q;
+    g_f_pre(i+1) = g_f(i) -0.5*log(det(2*pi*Q)) + 0.5*log(det(2*pi*M)) + 0.5*h_f(:,i)'*M*h_f(:,i);
+  end;
+end
+
+%%% Backward Message Passing
+h_b = zeros(L, T);
+K_b = zeros(L, L, T);
+g_b = zeros(1, T);
+
+h_b_post = zeros(L, T);
+K_b_post = zeros(L, L, T);
+g_b_post = zeros(1, T);
+
+for i=T:-1:1,
+  h_b(:,i) = h_b_post(:,i) + C'*inv_R*y(:,i);
+  K_b(:,:,i) = K_b_post(:,:,i) + C'*inv_R*C;
+  g_b(i) = g_b_post(i) - 0.5*log(det(2*pi*R)) - 0.5*y(:,i)'*inv_R*y(:,i);
+  if i>1,
+    M = inv(inv_Q + K_b(:,:,i));
+    h_b_post(:,i-1) = A'*inv(Q)*M*h_b(:,i);
+    K_b_post(:,:,i-1) = A'*inv_Q*(Q - M)*inv_Q*A;
+    g_b_post(i-1) = g_b(i) -0.5*log(det(2*pi*Q)) + 0.5*log(det(2*pi*M)) + 0.5*h_b(:,i)'*M*h_b(:,i);
+  end;
+end;
+
+
+%%%% Smoothed Estimates
+
+mu = zeros(size(h_f));
+Sig = zeros(size(K_f));
+g = zeros(size(g_f));
+lalpha = zeros(size(g_f));
+
+for i=1:T,
+  Sig(:,:,i) = inv(K_b_post(:,:,i) + K_f(:,:,i));
+  mu(:,i) = Sig(:,:,i)*(h_b_post(:,i) + h_f(:,i));
+  g(i) = g_b_post(i) + g_f(:,i);
+  lalpha(i) = g(i) + 0.5*log(det(2*pi*Sig(:,:,i))) + 0.5*mu(:,i)'*inv(Sig(:,:,i))*mu(:,i);
+end;
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